**Natural Language Processing Project**

The Emotional Side of Social Media Posts: Suicide Prediction based on Reddit Posts using Natural Language Processing Techniques

#Importing necessary libraries to be used

from wordcloud import WordCloud

import numpy as np 
import pandas as pd
from sklearn.model_selection import train_test_split
!pip install neattext
import neattext.functions as nfx
import matplotlib.pyplot as plt
import plotly.express as plx
from sklearn.metrics import classification_report
import keras
from keras.layers import Embedding,Dense,LSTM,Bidirectional,GlobalMaxPooling1D,Input,Dropout
from keras.callbacks import EarlyStopping,ReduceLROnPlateau
from keras.models import Sequential
import tensorflow as tf
from sklearn.preprocessing import LabelEncoder
from keras.preprocessing.text import Tokenizer
from keras.preprocessing.sequence import pad_sequences
from tqdm import tqdm
import seaborn as sns
import pickle
import warnings
warnings.filterwarnings('ignore')

Requirement already satisfied: neattext in c:\users\admin\anaconda3\lib\site-packages (0.1.0)

Using TensorFlow backend.

#importing dataset into a variable named data and viewing the top 5 records

data=pd.read_csv('Suicide_Detection.csv')
data.head()

	Unnamed: 0	text	class
0	2	Ex Wife Threatening SuicideRecently I left my ...	suicide
1	3	Am I weird I don't get affected by compliments...	non-suicide
2	4	Finally 2020 is almost over... So I can never ...	non-suicide
3	8	i need helpjust help me im crying so hard	suicide
4	9	I’m so lostHello, my name is Adam (16) and I’v...	suicide

# Counting the number of items belonging to different classes.

data['class'].value_counts()

non-suicide    116037
suicide        116037
Name: class, dtype: int64

data['class'].value_counts().index.values

array(['non-suicide', 'suicide'], dtype=object)

# Splitting the data into training and test set in a percentage ratio of 67%-33%

train_data,test_data=train_test_split(data,test_size=0.33,random_state=10)

train_data['class'].value_counts().index.values

array(['suicide', 'non-suicide'], dtype=object)

plx.bar(train_data,x=train_data['class'].value_counts().index.values,
        y=train_data['class'].value_counts(),color=['Suicide','Not Suicide'])

plt.figure(figsize=(12,10))
plt.pie(train_data['class'].value_counts(),startangle=90,colors=['#00dddf','#000fbb'],
        autopct='%0.2f%%',labels=['suicide','Not Suicide'])
plt.title('SUICIDE OR NOT ?',fontdict={'size':20})
plt.show()

#Cleaning the text by removing special characters and stopwords

def clean_text(text):
    text_length=[]
    cleaned_text=[]
    for sent in tqdm(text):
        sent=sent.lower()
        sent=nfx.remove_special_characters(sent)
        sent=nfx.remove_stopwords(sent)
#         sent=nfx.remove_shortwords(sent)
        text_length.append(len(sent.split()))
        cleaned_text.append(sent)
    return cleaned_text,text_length

cleaned_train_text,train_text_length=clean_text(train_data.text)
cleaned_test_text,test_text_length=clean_text(test_data.text)

100%|████████████████████████████████████████████████████████████████████████| 155489/155489 [00:22<00:00, 6984.71it/s]
100%|██████████████████████████████████████████████████████████████████████████| 76585/76585 [00:11<00:00, 6769.52it/s]

plt.figure(figsize=(12,10))
sns.distplot(train_text_length)
# plt.axis([-10,100,0,0.03])
plt.show()

# Tokenization of the sentences/post. Tokenization is essentially splitting a phrase, sentence, paragraph, or an entire text document into smaller units, such as individual words or terms. Each of these smaller units are called tokens.

tokenizer=Tokenizer()
tokenizer.fit_on_texts(cleaned_train_text)

# Frequency of words found in posts.

word_freq=pd.DataFrame(tokenizer.word_counts.items(),columns=['word','count']).sort_values(by='count',ascending=False)

plt.figure(figsize=(15,20))
sns.barplot(x='count',y='word',data=word_freq.iloc[:50])
plt.show()

# WordClouds are visual representations of words that give greater prominence to words that appear more frequently.

feature_names=word_freq['word'].values
wc=WordCloud(max_words=400)
wc.generate(' '.join(word for word in feature_names[500:3500] ))
plt.figure(figsize=(20,15))
plt.axis('off')
plt.imshow(wc)

<matplotlib.image.AxesImage at 0x2f29d106160>

# Sequence Padding, (pad_sequences) is used to ensure that all sequences in a list have the same length.

train_text_seq=tokenizer.texts_to_sequences(cleaned_train_text)
train_text_pad=pad_sequences(train_text_seq,maxlen=50)


test_text_seq=tokenizer.texts_to_sequences(cleaned_test_text)
test_text_pad=pad_sequences(test_text_seq,maxlen=50)

lbl_target=LabelEncoder()
train_output=lbl_target.fit_transform(train_data['class'])
test_output=lbl_target.transform(test_data['class'])

with open('glove.840B.300d.pkl', 'rb') as fp:
    glove_embedding = pickle.load(fp)

v=len(tokenizer.word_index)

embedding_matrix=np.zeros((v+1,300), dtype=float)
for word,idx in tokenizer.word_index.items():
    embedding_vector=glove_embedding.get(word)
    if embedding_vector is not None:
        embedding_matrix[idx]=embedding_vector

KeyboardInterrupt

early_stop=EarlyStopping(patience=5)
reducelr=ReduceLROnPlateau(patience=3)

model=Sequential()
## model.add(keras.Input(shape=(40,)))
model.add(Embedding(v+1,300,weights=[embedding_matrix],trainable=False))
model.add(LSTM(20,return_sequences=True))
model.add(GlobalMaxPooling1D())
model.add(Dropout(0.3))
model.add(Dense(256,activation='relu'))
model.add(Dropout(0.2))
model.add(Dense(1,activation='sigmoid'))
model.compile(optimizer=keras.optimizers.SGD(0.1,momentum=0.09),loss='binary_crossentropy',metrics=['accuracy'])

WARNING:tensorflow:From C:\Users\Admin\Anaconda3\lib\site-packages\tensorflow\python\ops\nn_impl.py:180: add_dispatch_support.<locals>.wrapper (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use tf.where in 2.0, which has the same broadcast rule as np.where

r=model.fit(train_text_pad,train_output,validation_data=(test_text_pad,test_output),
            epochs=20,batch_size=256,callbacks=[early_stop,reducelr])

Train on 155489 samples, validate on 76585 samples
Epoch 1/20
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Epoch 2/20
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val_loss: 0.2347 - val_accuracy: 0.9074
Epoch 3/20
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accuracy: 0.90 - ETA: 0s - loss: 0.2507 - accuracy: 0.90 - 46s 295us/step - loss: 0.2507 - accuracy: 0.9023 - val_loss: 0.2279 - val_accuracy: 0.9099
Epoch 4/20
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Epoch 5/20
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Epoch 6/20
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Epoch 7/20
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Epoch 8/20
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Epoch 9/20
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Epoch 10/20
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Epoch 11/20
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accuracy: 0.92 - ETA: 4s - loss: 0.1883 - accuracy: 0.92 - ETA: 4s - loss: 0.1884 - accuracy: 0.92 - ETA: 4s - loss: 0.1884 - accuracy: 0.92 - ETA: 4s - loss: 0.1886 - accuracy: 0.92 - ETA: 3s - loss: 0.1886 - accuracy: 0.92 - ETA: 3s - loss: 0.1886 - accuracy: 0.92 - ETA: 3s - loss: 0.1885 - accuracy: 0.92 - ETA: 3s - loss: 0.1885 - accuracy: 0.92 - ETA: 3s - loss: 0.1886 - accuracy: 0.92 - ETA: 3s - loss: 0.1885 - accuracy: 0.92 - ETA: 3s - loss: 0.1885 - accuracy: 0.92 - ETA: 3s - loss: 0.1886 - accuracy: 0.92 - ETA: 3s - loss: 0.1886 - accuracy: 0.92 - ETA: 3s - loss: 0.1887 - accuracy: 0.92 - ETA: 3s - loss: 0.1886 - accuracy: 0.92 - ETA: 3s - loss: 0.1887 - accuracy: 0.92 - ETA: 3s - loss: 0.1887 - accuracy: 0.92 - ETA: 3s - loss: 0.1887 - accuracy: 0.92 - ETA: 3s - loss: 0.1888 - accuracy: 0.92 - ETA: 3s - loss: 0.1888 - accuracy: 0.92 - ETA: 2s - loss: 0.1887 - accuracy: 0.92 - ETA: 2s - loss: 0.1887 - accuracy: 0.92 - ETA: 2s - loss: 0.1888 - accuracy: 0.92 - ETA: 2s - loss: 0.1887 - 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accuracy: 0.92 - ETA: 1s - loss: 0.1888 - accuracy: 0.92 - ETA: 1s - loss: 0.1888 - accuracy: 0.92 - ETA: 1s - loss: 0.1889 - accuracy: 0.92 - ETA: 0s - loss: 0.1889 - accuracy: 0.92 - ETA: 0s - loss: 0.1889 - accuracy: 0.92 - ETA: 0s - loss: 0.1888 - accuracy: 0.92 - ETA: 0s - loss: 0.1889 - accuracy: 0.92 - ETA: 0s - loss: 0.1888 - accuracy: 0.92 - ETA: 0s - loss: 0.1888 - accuracy: 0.9274155489/155489 [==============================] - ETA: 0s - loss: 0.1887 - accuracy: 0.92 - ETA: 0s - loss: 0.1887 - accuracy: 0.92 - ETA: 0s - loss: 0.1886 - accuracy: 0.92 - ETA: 0s - loss: 0.1885 - accuracy: 0.92 - ETA: 0s - loss: 0.1885 - accuracy: 0.92 - ETA: 0s - loss: 0.1886 - accuracy: 0.92 - ETA: 0s - loss: 0.1886 - accuracy: 0.92 - ETA: 0s - loss: 0.1886 - accuracy: 0.92 - ETA: 0s - loss: 0.1886 - accuracy: 0.92 - ETA: 0s - loss: 0.1886 - accuracy: 0.92 - 49s 312us/step - loss: 0.1886 - accuracy: 0.9275 - val_loss: 0.2231 - val_accuracy: 0.9174
Epoch 12/20
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accuracy: 0.92 - ETA: 1s - loss: 0.1840 - accuracy: 0.92 - ETA: 1s - loss: 0.1840 - accuracy: 0.92 - ETA: 0s - loss: 0.1840 - accuracy: 0.92 - ETA: 0s - loss: 0.1840 - accuracy: 0.92 - ETA: 0s - loss: 0.1840 - accuracy: 0.92 - ETA: 0s - loss: 0.1839 - accuracy: 0.92 - ETA: 0s - loss: 0.1840 - accuracy: 0.92 - ETA: 0s - loss: 0.1840 - accuracy: 0.92 - ETA: 0s - loss: 0.1840 - accuracy: 0.92 - ETA: 0s - loss: 0.1840 - accuracy: 0.92 - ETA: 0s - loss: 0.1840 - accuracy: 0.92 - ETA: 0s - loss: 0.1839 - accuracy: 0.92 - ETA: 0s - loss: 0.1840 - accuracy: 0.92 - ETA: 0s - loss: 0.1840 - accuracy: 0.92 - ETA: 0s - loss: 0.1841 - accuracy: 0.92 - ETA: 0s - loss: 0.1840 - accuracy: 0.92 - ETA: 0s - loss: 0.1839 - accuracy: 0.92 - 53s 341us/step - loss: 0.1840 - accuracy: 0.9298 - val_loss: 0.2032 - val_accuracy: 0.9237
Epoch 13/20
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accuracy: 0.93 - ETA: 0s - loss: 0.1793 - accuracy: 0.93 - 51s 330us/step - loss: 0.1793 - accuracy: 0.9316 - val_loss: 0.2410 - val_accuracy: 0.9046
Epoch 14/20
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accuracy: 0.93 - ETA: 0s - loss: 0.1734 - accuracy: 0.93 - ETA: 0s - loss: 0.1735 - accuracy: 0.93 - ETA: 0s - loss: 0.1735 - accuracy: 0.93 - ETA: 0s - loss: 0.1735 - accuracy: 0.93 - ETA: 0s - loss: 0.1735 - accuracy: 0.93 - ETA: 0s - loss: 0.1735 - accuracy: 0.93 - ETA: 0s - loss: 0.1736 - accuracy: 0.93 - ETA: 0s - loss: 0.1735 - accuracy: 0.93 - ETA: 0s - loss: 0.1736 - accuracy: 0.93 - ETA: 0s - loss: 0.1736 - accuracy: 0.93 - 49s 318us/step - loss: 0.1736 - accuracy: 0.9340 - val_loss: 0.1901 - val_accuracy: 0.9279
Epoch 15/20
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Epoch 16/20
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val_loss: 0.1876 - val_accuracy: 0.9283
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Epoch 18/20
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accuracy: 0.93 - ETA: 0s - loss: 0.1712 - accuracy: 0.93 - ETA: 0s - loss: 0.1712 - accuracy: 0.93 - ETA: 0s - loss: 0.1713 - accuracy: 0.93 - ETA: 0s - loss: 0.1713 - accuracy: 0.93 - ETA: 0s - loss: 0.1714 - accuracy: 0.93 - ETA: 0s - loss: 0.1714 - accuracy: 0.93 - ETA: 0s - loss: 0.1714 - accuracy: 0.93 - 53s 341us/step - loss: 0.1714 - accuracy: 0.9344 - val_loss: 0.1920 - val_accuracy: 0.9269
Epoch 19/20
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accuracy: 0.93 - ETA: 1s - loss: 0.1709 - accuracy: 0.93 - ETA: 1s - loss: 0.1708 - accuracy: 0.93 - ETA: 1s - loss: 0.1708 - accuracy: 0.93 - ETA: 1s - loss: 0.1708 - accuracy: 0.93 - ETA: 1s - loss: 0.1708 - accuracy: 0.93 - ETA: 1s - loss: 0.1708 - accuracy: 0.93 - ETA: 1s - loss: 0.1708 - accuracy: 0.93 - ETA: 1s - loss: 0.1708 - accuracy: 0.93 - ETA: 0s - loss: 0.1708 - accuracy: 0.93 - ETA: 0s - loss: 0.1708 - accuracy: 0.93 - ETA: 0s - loss: 0.1709 - accuracy: 0.93 - ETA: 0s - loss: 0.1710 - accuracy: 0.93 - ETA: 0s - loss: 0.1710 - accuracy: 0.93 - ETA: 0s - loss: 0.1709 - accuracy: 0.93 - ETA: 0s - loss: 0.1709 - accuracy: 0.93 - ETA: 0s - loss: 0.1709 - accuracy: 0.93 - ETA: 0s - loss: 0.1710 - accuracy: 0.93 - ETA: 0s - loss: 0.1710 - accuracy: 0.93 - ETA: 0s - loss: 0.1710 - accuracy: 0.93 - ETA: 0s - loss: 0.1711 - accuracy: 0.93 - ETA: 0s - loss: 0.1712 - accuracy: 0.93 - ETA: 0s - loss: 0.1711 - accuracy: 0.93 - ETA: 0s - loss: 0.1711 - accuracy: 0.93 - 52s 335us/step - loss: 0.1711 - accuracy: 0.9352 - val_loss: 0.1891 - val_accuracy: 0.9280
Epoch 20/20
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plt.figure(figsize=(10,8))
plt.plot(r.history['accuracy'])
plt.plot(r.history['val_accuracy'])
plt.title('ACCURACY CURVE',fontdict={'size':20})
plt.show()

plt.figure(figsize=(8,10))
plt.plot(r.history['loss'])
plt.plot(r.history['val_loss'])
plt.title('LOSS CURVE',fontdict={'size':20})
plt.show()